M/Ph18dZddlZddlmZGddZdS)aOGenerate a random process with panel structure Created on Sat Dec 17 22:15:27 2011 Author: Josef Perktold Notes ----- * written with unbalanced panels in mind, but not flexible enough yet * need more shortcuts and options for balanced panel * need to add random intercept or coefficients * only one-way (repeated measures) so far N)correlation_structuresc<eZdZdZddejdddfdZdZdZdS) PanelSampleaedata generating process for panel with within correlation allows various within correlation structures, but no random intercept yet Parameters ---------- nobs : int total number of observations k_vars : int number of explanatory variables to create in exog, including constant n_groups int number of groups in balanced sample exog : None or ndarray default is None, in which case a exog is created within : bool If True (default), then the exog vary within a group. If False, then only variation across groups is used. TODO: this option needs more work corr_structure : ndarray or ?? Default is np.eye. corr_args : tuple arguments for the corr_structure scale : float scale of noise, standard deviation of normal distribution seed : None or int If seed is given, then this is used to create the random numbers for the sample. Notes ----- The behavior for panel robust covariance estimators seems to differ by a large amount by whether exog have mostly within group or across group variation. I do not understand why this should be the case from the theory, and this would warrant more investigation. This is just used in one example so far and needs more usage to see what will be useful to add. NTrc n||z} | |z}||_| |_||_||_||_t jt j|| |_t j|dz| z|_ |v|r*t j t j dd| |} n)t jt j dd| |} | dddft j|z}||_ d|_ d|_| t jdd} | |_t j| |_|t j| z|_|j| g|R} t-j| |j|_t j||_dS)Nrri?B)nobsnobs_in_groupsk_varscorr_structurenprepeatarangegroups group_indicestilelinspaceexogy_truebetarandomrandintseed RandomState random_stateonesstdcscorr2covcovzeros group_means) selfr rr rwithinr corr_argsscalerr tcorrs f/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/sandbox/panel/random_panel.py__init__zPanelSample.__init__>sx      ,i ( 3 3V<< Yxz22V; < BGBK1f55x@@Ibk"Qv66AAQQQtV9bi///D   <9$$Q//D I11$77276??*"t"66I666;tTX..8H--c|j!|jd|_dSt j|j|j|_dS)Nr)rrsumrrdot)r&s r, get_y_truezPanelSample.get_y_trueos> 9 )--**DKKK&DI66DKKKr.c|j}|j||j}|j}d}|rd|jt j||j| }|t j |j |z }nt j |j t j}|t jt#|jD]c}|j||dz\}}|j |} |j| t j|jz|j|||<d|j|z} | S)zT generate endog for a random panel dataset with within correlation NT)sizer )rrr2r r multivariate_normalrr$r#ravelrr%emptyr float64fillnanrangerr) r&rr r use_balancednoiseiiidxidxuppmean_iendogs r,generate_panelzPanelSample.generate_panelvsW " ;  OO   =  Q%99"(6:J:J26(7?:AAAF  RYt/88 8EEHTY 33E JJrv   DM** Q Q"0BqD9 V)"-$($5$I$I(.1E1E(Etx%Q%Qc&j!! e# r.) __name__ __module__ __qualname____doc__reyer-r2rCrr.r,rrsd&&P59 ""AD........b777!!!!!r.r)rGnumpyrrr!rrr.r,rKsk ******BBBBBBBBBBr.